Forwarded from Remote Jobs
Data Science & Analytics Job Opportunities -
Actively job hunting? Here's a curated list of open roles — from entry-level to senior positions:
💼 Open Roles:
1️⃣ Data Analyst – Newell Brands (📍Atlanta, GA)
🧑💼 Entry-level (— around 1-3 year experience)
🔗 Apply here : https://lnkd.in/ewEjFqYa
2️⃣ Revenue Operational Analyst – Field Nation (📍Remote)
🧑💼 Entry-Mid-level (— around 2-4 years experience)
🔗 Apply here : https://lnkd.in/eMk6MhNP
3️⃣ Data Analyst – DTE Energy (📍Detroit, MI)
🧑💼 Entry-level (— around 3+ years experience)
🔗 Apply here : https://lnkd.in/emEzNZkv
4️⃣ Data Analytics – City of Philadelphia (📍Philadelphia, PA)
🧑💼 Entry-level (— around 3-5 year experience)
🔗 Apply here : https://lnkd.in/eicgiwsB
5️⃣ BI Engineer – Jackson Health System (📍Miami, FL)
🧑💼 Entry-Mid-level (— around 3 year experience)
🔗 Apply here : https://lnkd.in/e4NXkYgQ
6️⃣ Analyst – ArchWell Health (📍Nashville, TN)
🧑💼 Entry-Level (— around 1-2 year experience)
🔗 Apply here : https://lnkd.in/eh_aUHmh
7️⃣ Data scientist I – Harris County Sheriff's Office (📍Des Moines, IA )
🧑💼 Entry-level (— around 1 year experience)
🔗 Apply here : https://lnkd.in/eWc8GWZd
8️⃣ Financial Analyst II – Dignity Health (📍Chandler, AZ)
🧑💼 Entry-level (— around 1 year experience)
🔗 Apply here : https://lnkd.in/eWvXEJ-U
9️⃣ BI Analyst – Integrated Services for Behavioral Health (📍McArthur, OH)
🧑💼 Mid-level (— around 3-4 years experience)
🔗 Apply here : https://lnkd.in/eWrd5uTQ
🔟 Data Engineer – Costco Wholesale (📍Seattle, WA)
🧑💼 Entry-level (— around 2 years experience)
🔗 Apply here : https://lnkd.in/eyrggt3u
Actively job hunting? Here's a curated list of open roles — from entry-level to senior positions:
💼 Open Roles:
1️⃣ Data Analyst – Newell Brands (📍Atlanta, GA)
🧑💼 Entry-level (— around 1-3 year experience)
🔗 Apply here : https://lnkd.in/ewEjFqYa
2️⃣ Revenue Operational Analyst – Field Nation (📍Remote)
🧑💼 Entry-Mid-level (— around 2-4 years experience)
🔗 Apply here : https://lnkd.in/eMk6MhNP
3️⃣ Data Analyst – DTE Energy (📍Detroit, MI)
🧑💼 Entry-level (— around 3+ years experience)
🔗 Apply here : https://lnkd.in/emEzNZkv
4️⃣ Data Analytics – City of Philadelphia (📍Philadelphia, PA)
🧑💼 Entry-level (— around 3-5 year experience)
🔗 Apply here : https://lnkd.in/eicgiwsB
5️⃣ BI Engineer – Jackson Health System (📍Miami, FL)
🧑💼 Entry-Mid-level (— around 3 year experience)
🔗 Apply here : https://lnkd.in/e4NXkYgQ
6️⃣ Analyst – ArchWell Health (📍Nashville, TN)
🧑💼 Entry-Level (— around 1-2 year experience)
🔗 Apply here : https://lnkd.in/eh_aUHmh
7️⃣ Data scientist I – Harris County Sheriff's Office (📍Des Moines, IA )
🧑💼 Entry-level (— around 1 year experience)
🔗 Apply here : https://lnkd.in/eWc8GWZd
8️⃣ Financial Analyst II – Dignity Health (📍Chandler, AZ)
🧑💼 Entry-level (— around 1 year experience)
🔗 Apply here : https://lnkd.in/eWvXEJ-U
9️⃣ BI Analyst – Integrated Services for Behavioral Health (📍McArthur, OH)
🧑💼 Mid-level (— around 3-4 years experience)
🔗 Apply here : https://lnkd.in/eWrd5uTQ
🔟 Data Engineer – Costco Wholesale (📍Seattle, WA)
🧑💼 Entry-level (— around 2 years experience)
🔗 Apply here : https://lnkd.in/eyrggt3u
❤2👍1
Forwarded from Python for Data Analysts
𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: 𝟱 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍
Want to break into Data Science but don’t know where to begin?👨💻📌
You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SU5FJ0
No prior experience needed!✅️
Want to break into Data Science but don’t know where to begin?👨💻📌
You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SU5FJ0
No prior experience needed!✅️
❤3
𝟱 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 💻
You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.
Here’s what to focus on (with examples) 👇
🔹 1. String Manipulation (Common in Data Cleaning)
✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
🔹 2. GroupBy and Aggregation with Pandas
✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly
Example: “What’s the top-selling product in each region?”
🔹 3. SQL Join + Window Functions
✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries
Example: “Get 2nd highest salary in each department”
🔹 4. Data Structures: Lists, Dicts, Sets in Python
✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions
Example: “Count frequency of hashtags in tweets”
🔹 5. Basic Algorithms (Not DP or Graphs)
✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays
Example: “Detect if a pair of values sum to 100”
🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
You don’t need to be a LeetCode grandmaster.
But data science interviews still test your problem-solving mindset—and these 5 types of challenges are the ones that actually matter.
Here’s what to focus on (with examples) 👇
🔹 1. String Manipulation (Common in Data Cleaning)
✅ Parse messy columns (e.g., split “Name_Age_City”)
✅ Regex to extract phone numbers, emails, URLs
✅ Remove stopwords or HTML tags in text data
Example: Clean up a scraped dataset from LinkedIn bias
🔹 2. GroupBy and Aggregation with Pandas
✅ Group sales data by product/region
✅ Calculate avg, sum, count using .groupby()
✅ Handle missing values smartly
Example: “What’s the top-selling product in each region?”
🔹 3. SQL Join + Window Functions
✅ INNER JOIN, LEFT JOIN to merge tables
✅ ROW_NUMBER(), RANK(), LEAD(), LAG() for trends
✅ Use CTEs to break complex queries
Example: “Get 2nd highest salary in each department”
🔹 4. Data Structures: Lists, Dicts, Sets in Python
✅ Use dictionaries to map, filter, and count
✅ Remove duplicates with sets
✅ List comprehensions for clean solutions
Example: “Count frequency of hashtags in tweets”
🔹 5. Basic Algorithms (Not DP or Graphs)
✅ Sliding window for moving averages
✅ Two pointers for duplicate detection
✅ Binary search in sorted arrays
Example: “Detect if a pair of values sum to 100”
🎯 Tip: Practice challenges that feel like real-world data work, not textbook CS exams.
Use platforms like:
StrataScratch
Hackerrank (SQL + Python)
Kaggle Code
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
❤3
𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍
𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk
𝗝𝗮𝘃𝗮 :- https://pdlink.in/4dWkAMf
𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :- https://pdlink.in/4dFem3o
𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw
Get Your Dream Tech Job In Your Dream Company💫
𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk
𝗝𝗮𝘃𝗮 :- https://pdlink.in/4dWkAMf
𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :- https://pdlink.in/4dFem3o
𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw
Get Your Dream Tech Job In Your Dream Company💫
❤1
Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://news.1rj.ru/str/free4unow_backup
Like if you need similar content 😄👍
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://news.1rj.ru/str/free4unow_backup
Like if you need similar content 😄👍
❤3👍3
Real-world Data Science projects ideas: 💡📈
1. Credit Card Fraud Detection
📍 Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
📍 Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
📍 Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
📍 Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
📍 Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
📍 Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
📍 Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
👉 Pick 2–3 projects aligned with your interests.
👉 Document everything on GitHub, and post about your learnings on LinkedIn.
Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29
React ❤️ for more
1. Credit Card Fraud Detection
📍 Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
📍 Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
📍 Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
📍 Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
📍 Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
📍 Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
📍 Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
👉 Pick 2–3 projects aligned with your interests.
👉 Document everything on GitHub, and post about your learnings on LinkedIn.
Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29
React ❤️ for more
❤3
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 & 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱!😍
Ready to kickstart your career in Data Science—without spending a rupee?💰
These 4 beginner-friendly courses will help you build a strong foundation in data science by teaching you how to gather, clean, analyse, and visualise data📊📌
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
https://pdlink.in/45uXCtI
An initiative supported by NASSCOM and the Government of India✅️
Ready to kickstart your career in Data Science—without spending a rupee?💰
These 4 beginner-friendly courses will help you build a strong foundation in data science by teaching you how to gather, clean, analyse, and visualise data📊📌
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
https://pdlink.in/45uXCtI
An initiative supported by NASSCOM and the Government of India✅️
❤2
Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
𝟳 𝗥𝗲𝗮𝗱𝘆-𝘁𝗼-𝗨𝘀𝗲 𝗘𝗺𝗮𝗶𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 𝘁𝗼 𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀😍
📩 Struggling to write the perfect email to a recruiter?🗣
You’re not alone. The way you write your email can make or break your first impression🤝
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TtQh64
Having the right email format makes all the difference✅️
📩 Struggling to write the perfect email to a recruiter?🗣
You’re not alone. The way you write your email can make or break your first impression🤝
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TtQh64
Having the right email format makes all the difference✅️
Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://news.1rj.ru/str/free4unow_backup
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A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
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5⃣ frequently Asked SQL Interview Questions with Answers in data analyst interviews
📍1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).
📍2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.
📍3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).
📍4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.
;
📍5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).
Hope it helps :)
📍1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).
SELECT c.CustomerID, c. Name, AVG(o.Amount) AS AveragePurchase
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c. Name;
📍2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.
SELECT e1.DepartmentID, e1.EmployeeID, e1 .Name, e1.Salary
FROM Employees e1
WHERE Salary = (SELECT MIN(Salary) FROM Employees e2 WHERE e2.DepartmentID = e1.DepartmentID);
📍3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).
SELECT p.ProductID, p.ProductName
FROM Products p
LEFT JOIN Sales s ON p.ProductID = s.ProductID
WHERE s.ProductID IS NULL;
📍4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.
SELECT o.CustomerID, SUM(oi.Quantity) AS TotalQuantity
FROM Orders o
JOIN OrderItems oi ON o.OrderID = oi.OrderID
GROUP BY o.CustomerID
ORDER BY TotalQuantity DESC
LIMIT 1
;
📍5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).
SELECT CustomerID, MIN(OrderDate) AS EarliestOrderDate
FROM Orders
GROUP BY CustomerID
Hope it helps :)
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
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Forwarded from AI Prompts | ChatGPT | Google Gemini | Claude
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🔰 C++ Roadmap for Beginners 2025
├── 🧠 Introduction to C++ & How It Works
├── 🧰 Setting Up Environment (IDE, Compiler)
├── 📝 Basic Syntax & Structure
├── 🔢 Variables, Data Types & Constants
├── ➕ Operators (Arithmetic, Relational, Logical, Bitwise)
├── 🔁 Flow Control (if, else, switch)
├── 🔄 Loops (for, while, do...while)
├── 🧩 Functions (Declaration, Definition, Recursion)
├── 📦 Arrays, Strings & Vectors
├── 🧱 Pointers & References
├── 🧮 Dynamic Memory Allocation (new, delete)
├── 🏗 Structures & Unions
├── 🏛 Object-Oriented Programming (Classes, Objects, Inheritance, Polymorphism)
├── 📂 File Handling in C++
├── ⚠️ Exception Handling
├── 🧠 STL (Standard Template Library - vector, map, set, etc.)
├── 🧪 Mini Projects (Bank System, Student Record, etc.)
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#c #programming
├── 🧠 Introduction to C++ & How It Works
├── 🧰 Setting Up Environment (IDE, Compiler)
├── 📝 Basic Syntax & Structure
├── 🔢 Variables, Data Types & Constants
├── ➕ Operators (Arithmetic, Relational, Logical, Bitwise)
├── 🔁 Flow Control (if, else, switch)
├── 🔄 Loops (for, while, do...while)
├── 🧩 Functions (Declaration, Definition, Recursion)
├── 📦 Arrays, Strings & Vectors
├── 🧱 Pointers & References
├── 🧮 Dynamic Memory Allocation (new, delete)
├── 🏗 Structures & Unions
├── 🏛 Object-Oriented Programming (Classes, Objects, Inheritance, Polymorphism)
├── 📂 File Handling in C++
├── ⚠️ Exception Handling
├── 🧠 STL (Standard Template Library - vector, map, set, etc.)
├── 🧪 Mini Projects (Bank System, Student Record, etc.)
Like for the detailed explanation ❤️
#c #programming
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